Resume for Machine Learning Engineer Roles
ML engineering resumes get filtered by ML engineers, not generalist recruiters at most serious shops. They're scanning for two distinct signals: research depth (papers, novel work, real benchmarks) and shipping ability (production models, infra, scaling). Most resumes pretend to have both and convince of neither.
Pick a lane: research or applied
Lab roles want first-author papers, novel architectures, reproducible benchmarks. Applied/MLE roles want production models, training infrastructure, and serving stack experience. The same resume rarely lands both because the bullets that signal 'PhD-track researcher' read as overqualified for shipping work — and vice versa.
What to surface for applied ML roles
Show production-model experience, even if it's a side project that's actually deployed. Name the framework versions, the training infra (Vertex AI, SageMaker, Ray, custom), and the serving stack (Triton, Bento, FastAPI behind a queue). Bullets that namedrop 'PyTorch' without scaling specifics read as classroom work.
- Frameworks at depth: PyTorch (Lightning, FSDP), TensorFlow, JAX, vLLM
- Training infra: distributed training, mixed precision, gradient accumulation
- Serving: ONNX, TensorRT, quantization, batch inference, latency budgets
- Data: feature stores, streaming, label quality work
What to surface for research roles
First-author or co-authored work at top venues (NeurIPS, ICML, ICLR, ACL, CVPR, EMNLP). Even rejected papers you've put on arXiv count if the work is novel and reproducible. Open-source contributions to popular ML repos. Strong math coursework.
Examples
Applied ML bullet — before vs. after
- Before: Built a recommendation model using PyTorch and deployed it.
- After: Shipped two-tower retrieval model serving 18M req/day on candidate generation for video feed; reduced p99 latency 280ms → 95ms by switching from PyTorch eager to ONNX + Triton with FP16; lifted CTR 4.2% in 6-week A/B.
FAQ
Do I need published papers for an MLE role?
No. For applied/MLE roles at most companies, shipping signal beats publication count. Papers help — but a deployed system with measurable impact often wins.
Should I list every model architecture I've used?
List the ones you've trained from scratch or fine-tuned in production. 'Familiar with transformers' from a class isn't a credential at this point.
Are LeetCode-style algo skills relevant?
For interviews, yes. For the resume, no — same as SWE roles, leave the LeetCode count off.
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